mirror of https://github.com/THUDM/ChatGLM-6B
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39 lines
1.3 KiB
39 lines
1.3 KiB
from transformers import AutoModel, AutoTokenizer |
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import gradio as gr |
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) |
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda() |
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model = model.eval() |
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MAX_TURNS = 20 |
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MAX_BOXES = MAX_TURNS * 2 |
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def predict(input, history=[]): |
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response, history = model.chat(tokenizer, input, history) |
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updates = [] |
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for query, response in history: |
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updates.append(gr.update(visible=True, value=query)) |
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updates.append(gr.update(visible=True, value=response)) |
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if len(updates) < MAX_BOXES: |
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updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates)) |
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return [history] + updates |
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with gr.Blocks() as demo: |
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state = gr.State([]) |
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text_boxes = [] |
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for i in range(MAX_BOXES): |
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if i % 2 == 0: |
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label = "提问：" |
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else: |
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label = "回复：" |
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text_boxes.append(gr.Textbox(visible=False, label=label)) |
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with gr.Row(): |
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with gr.Column(scale=4): |
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txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False) |
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with gr.Column(scale=1): |
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button = gr.Button("Generate") |
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button.click(predict, [txt, state], [state] + text_boxes) |
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demo.queue().launch(share=True)
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